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Metals
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Machine Learning Models in Metals (2nd Edition)
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Machine Learning Models in Metals (2nd Edition)

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 2118

Special Issue Editor

Prof. Dr. Olivier Pantale

E-Mail Website
Guest Editor
Laboratoire Génie de Production/ENIT, Institut National Polytechnique de Toulouse, 65000 Tarbes, France
Interests: artificial neural networks; finite elements; metal forming; identification of behavior laws; programming; mechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational methods and simulations have greatly contributed to our understanding of the properties and behavior of metals. The integration of machine learning models, particularly neural networks, into the simulation of metal processes represents a significant stride forward in the field. In manufacturing, machine learning algorithms can optimize production processes by analyzing vast datasets in real-time, leading to increased efficiency and cost savings. Moreover, machine learning-driven material discovery has yielded exciting results, with algorithms identifying novel metal alloys with tailored properties for specific applications, such as lightweight yet strong materials for the aerospace industry. Additionally, characterizing complex microstructures and grain boundaries in metals has become more precise and efficient with neural networks, enabling researchers to better understand the relationship between microstructure and material performance. Overall, machine learning is transforming metallurgy, materials design, and numerous industries that rely on metals by accelerating innovation and enabling data-driven decision-making.

We invite researchers, scientists, and experts in the field to contribute to this Special Issue of Metals, entitled "Machine Learning Models in Metals (2nd Edition)". This Special Issue aims to provide a platform for the dissemination of cutting-edge research, novel methodologies, and innovative applications that harness the power of machine learning in the study of metals.

Suggested themes and article types for submissions

In this Special Issue, original research articles and reviews are welcome. Papers for this Special Issue should address, but are not limited to, the following topics:

Machine learning-driven material discovery for novel metal alloys;

Predictive modeling of mechanical properties, including tensile strength, hardness, and ductility;

Computational techniques for optimizing metal manufacturing processes;

Predictive modeling of metal corrosion and degradation;

Machine learning-based defect detection and quality control in metal production;

Data-driven approaches to understand metal–metal and metal–environment interactions;

Machine learning techniques for characterizing microstructures and grain boundaries in metals;

Applications of neural networks, deep learning, and reinforcement learning in metallurgy;

Data-driven insights into metal behavior under extreme conditions, such as high temperature or pressure.

Prof. Dr. Olivier Pantale
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial neural networks
  • machine learning in metallurgy
  • deep learning
  • data-driven materials science
  • manufacturing process modeling and simulation
  • metal alloy simulations
  • metal properties modeling
  • metal structure prediction
  • computational materials science
  • metal property prediction models

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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13 pages, 1987 KB  
Article
Machine Learning-Based Prediction of Young’s Modulus in Ti-Alloys
by Seza Dinibutun, Yousef Alshammari and Leandro Bolzoni
Metals 2026, 16(2), 233; https://doi.org/10.3390/met16020233 - 19 Feb 2026
Cited by 1 | Viewed by 726
Abstract
This study explores the use of machine learning to predict the experimental Young’s modulus of titanium alloys based on their mechanical and microstructural properties. Several regression models were developed and compared, including Random Forest, XGBoost, CatBoost, Multi-Layer Perceptron, and a Stacking Regressor. Among [...] Read more.
This study explores the use of machine learning to predict the experimental Young’s modulus of titanium alloys based on their mechanical and microstructural properties. Several regression models were developed and compared, including Random Forest, XGBoost, CatBoost, Multi-Layer Perceptron, and a Stacking Regressor. Among these, Random Forest, XGBoost and CatBoost achieved the most accurate results with R2 values above 0.85. To improve interpretability, SHapley Additive exPlanations were applied to examine which input features most strongly influenced the predictions. The results showed that yield strength, hardness, and the molybdenum equivalent parameter (moe) were among the most influential descriptors. While yield strength and hardness were positively associated with the predicted values, higher moe values corresponded to lower predicted Young’s modulus. This study focuses on the prediction of Young’s modulus, a comparatively less explored elastic property in Ti-alloy machine learning studies and combines systematic model comparison with SHAP-based interpretability to provide physically consistent insights into feature–property relationships. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals (2nd Edition))
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28 pages, 70123 KB  
Article
Synthetic Rebalancing of Imbalanced Macro Etch Testing Data for Deep Learning Image Classification
by Yann Niklas Schöbel, Martin Müller and Frank Mücklich
Metals 2025, 15(11), 1172; https://doi.org/10.3390/met15111172 - 23 Oct 2025
Viewed by 874
Abstract
The adoption of artificial intelligence (AI) in industrial manufacturing lags behind research progress, partly due to smaller, imbalanced datasets derived from real processes. In non-destructive aerospace testing, this challenge is amplified by the low defect rates of high-quality manufacturing. This study evaluates the [...] Read more.
The adoption of artificial intelligence (AI) in industrial manufacturing lags behind research progress, partly due to smaller, imbalanced datasets derived from real processes. In non-destructive aerospace testing, this challenge is amplified by the low defect rates of high-quality manufacturing. This study evaluates the use of synthetic data, generated via multiresolution stochastic texture synthesis, to mitigate class imbalance in material defect classification for the superalloy Inconel 718. Multiple datasets with increasing imbalance were sampled, and an image classification model was tested under three conditions: native data, data augmentation, and synthetic data inclusion. Additionally, round robin tests with experts assessed the realism and quality of synthetic samples. Results show that synthetic data significantly improved model performance on highly imbalanced datasets. Expert evaluations provided insights into identifiable artificial properties and class-specific accuracy. Finally, a quality assessment model was implemented to filter low-quality synthetic samples, further boosting classification performance to near the balanced reference level. These findings demonstrate that synthetic data generation, combined with quality control, is an effective strategy for addressing class imbalance in industrial AI applications. Full article
(This article belongs to the Special Issue Machine Learning Models in Metals (2nd Edition))
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